21 research outputs found

    Equal time for data on the internet with websemantics

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    Linked Data - the story so far

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    The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward

    Metadata-based and personalized web querying

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    Cataloged from PDF version of article.The advent of the Web has raised new searching and querying problems. Keyword matching based querying techniques that have been widely used by search engines, return thousands of Web documents for a single query, and most of these documents are generally unrelated to the users’ information needs. Towards the goal of improving the information search needs of Web users, a recent promising approach is to index the Web by using metadata and annotations. In this thesis, we model and query Web-based information resources using metadata for improved Web searching capabilities. Employing metadata for querying the Web increases the precision of the query outputs by returning semantically more meaningful results. Our Web data model, named “Web information space model”, consists of Web-based information resources (HTML/XML documents on the Web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles that indicate users’ preferences about experts as well as users’ knowledge about topics). Expert advice is specified using topics and relationships among topics (i.e., metalinks), along the lines of recently proposed topic maps standard. Topics and metalinks constitute metadata that describe the contents of the underlying Web information resources. Experts assign scores to topics, metalinks, and information resources to represent the “importance” of them. User profiles store users’ preferences and navigational history information about the information resources that the user visits. User preferences, knowledge level on topics, and history information are used for personalizing the Web search, and improving the precision of the results returned to the user. We store expert advices and user profiles in an object relational database iv v management system, and extend the SQL for efficient querying of Web-based information resources through the Web information space model. SQL extensions include the clauses for propagating input importance scores to output tuples, the clause that specifies query stopping condition, and new operators (i.e., text similarity based selection, text similarity based join, and topic closure). Importance score propagation and query stopping condition allow ranking of query outputs, and limiting the output size. Text similarity based operators and topic closure operator support sophisticated querying facilities. We develop a new algebra called Sideway Value generating Algebra (SVA) to process these SQL extensions. We also propose evaluation algorithms for the text similarity based SVA directional join operator, and report experimental results on the performance of the operator. We demonstrate experimentally the effectiveness of metadata-based personalized Web search through SQL extensions over the Web information space model against keyword matching based Web search techniques.Özel, Selma AyşePh.D

    ISCR annual report FY 1998

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    Resource discovery in heterogeneous digital content environments

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    The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice

    Creation, Enrichment and Application of Knowledge Graphs

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    The world is in constant change, and so is the knowledge about it. Knowledge-based systems - for example, online encyclopedias, search engines and virtual assistants - are thus faced with the constant challenge of collecting this knowledge and beyond that, to understand it and make it accessible to their users. Only if a knowledge-based system is capable of this understanding - that is, it is capable of more than just reading a collection of words and numbers without grasping their semantics - it can recognise relevant information and make it understandable to its users. The dynamics of the world play a unique role in this context: Events of various kinds which are relevant to different communities are shaping the world, with examples ranging from the coronavirus pandemic to the matches of a local football team. Vital questions arise when dealing with such events: How to decide which events are relevant, and for whom? How to model these events, to make them understood by knowledge-based systems? How is the acquired knowledge returned to the users of these systems? A well-established concept for making knowledge understandable by knowledge-based systems are knowledge graphs, which contain facts about entities (persons, objects, locations, ...) in the form of graphs, represent relationships between these entities and make the facts understandable by means of ontologies. This thesis considers knowledge graphs from three different perspectives: (i) Creation of knowledge graphs: Even though the Web offers a multitude of sources that provide knowledge about the events in the world, the creation of an event-centric knowledge graph requires recognition of such knowledge, its integration across sources and its representation. (ii) Knowledge graph enrichment: Knowledge of the world seems to be infinite, and it seems impossible to grasp it entirely at any time. Therefore, methods that autonomously infer new knowledge and enrich the knowledge graphs are of particular interest. (iii) Knowledge graph interaction: Even having all knowledge of the world available does not have any value in itself; in fact, there is a need to make it accessible to humans. Based on knowledge graphs, systems can provide their knowledge with their users, even without demanding any conceptual understanding of knowledge graphs from them. For this to succeed, means for interaction with the knowledge are required, hiding the knowledge graph below the surface. In concrete terms, I present EventKG - a knowledge graph that represents the happenings in the world in 15 languages - as well as Tab2KG - a method for understanding tabular data and transforming it into a knowledge graph. For the enrichment of knowledge graphs without any background knowledge, I propose HapPenIng, which infers missing events from the descriptions of related events. I demonstrate means for interaction with knowledge graphs at the example of two web-based systems (EventKG+TL and EventKG+BT) that enable users to explore the happenings in the world as well as the most relevant events in the lives of well-known personalities.Die Welt befindet sich im steten Wandel, und mit ihr das Wissen über die Welt. Wissensbasierte Systeme - seien es Online-Enzyklopädien, Suchmaschinen oder Sprachassistenten - stehen somit vor der konstanten Herausforderung, dieses Wissen zu sammeln und darüber hinaus zu verstehen, um es so Menschen verfügbar zu machen. Nur wenn ein wissensbasiertes System in der Lage ist, dieses Verständnis aufzubringen - also zu mehr in der Lage ist, als auf eine unsortierte Ansammlung von Wörtern und Zahlen zurückzugreifen, ohne deren Bedeutung zu erkennen -, kann es relevante Informationen erkennen und diese seinen Nutzern verständlich machen. Eine besondere Rolle spielt hierbei die Dynamik der Welt, die von Ereignissen unterschiedlichster Art geformt wird, die für unterschiedlichste Bevölkerungsgruppe relevant sind; Beispiele hierfür erstrecken sich von der Corona-Pandemie bis hin zu den Spielen lokaler Fußballvereine. Doch stellen sich hierbei bedeutende Fragen: Wie wird die Entscheidung getroffen, ob und für wen derlei Ereignisse relevant sind? Wie sind diese Ereignisse zu modellieren, um von wissensbasierten Systemen verstanden zu werden? Wie wird das angeeignete Wissen an die Nutzer dieser Systeme zurückgegeben? Ein bewährtes Konzept, um wissensbasierten Systemen das Wissen verständlich zu machen, sind Wissensgraphen, die Fakten über Entitäten (Personen, Objekte, Orte, ...) in der Form von Graphen sammeln, Zusammenhänge zwischen diesen Entitäten darstellen, und darüber hinaus anhand von Ontologien verständlich machen. Diese Arbeit widmet sich der Betrachtung von Wissensgraphen aus drei aufeinander aufbauenden Blickwinkeln: (i) Erstellung von Wissensgraphen: Auch wenn das Internet eine Vielzahl an Quellen anbietet, die Wissen über Ereignisse in der Welt bereithalten, so erfordert die Erstellung eines ereigniszentrierten Wissensgraphen, dieses Wissen zu erkennen, miteinander zu verbinden und zu repräsentieren. (ii) Anreicherung von Wissensgraphen: Das Wissen über die Welt scheint schier unendlich und so scheint es unmöglich, dieses je vollständig (be)greifen zu können. Von Interesse sind also Methoden, die selbstständig das vorhandene Wissen erweitern. (iii) Interaktion mit Wissensgraphen: Selbst alles Wissen der Welt bereitzuhalten, hat noch keinen Wert in sich selbst, vielmehr muss dieses Wissen Menschen verfügbar gemacht werden. Basierend auf Wissensgraphen, können wissensbasierte Systeme Nutzern ihr Wissen darlegen, auch ohne von diesen ein konzeptuelles Verständis von Wissensgraphen abzuverlangen. Damit dies gelingt, sind Möglichkeiten der Interaktion mit dem gebotenen Wissen vonnöten, die den genutzten Wissensgraphen unter der Oberfläche verstecken. Konkret präsentiere ich EventKG - einen Wissensgraphen, der Ereignisse in der Welt repräsentiert und in 15 Sprachen verfügbar macht, sowie Tab2KG - eine Methode, um in Tabellen enthaltene Daten anhand von Hintergrundwissen zu verstehen und in Wissensgraphen zu wandeln. Zur Anreicherung von Wissensgraphen ohne weiteres Hintergrundwissen stelle ich HapPenIng vor, das fehlende Ereignisse aus den vorliegenden Beschreibungen ähnlicher Ereignisse inferiert. Interaktionsmöglichkeiten mit Wissensgraphen demonstriere ich anhand zweier web-basierter Systeme (EventKG+TL und EventKG+BT), die Nutzern auf einfache Weise die Exploration von Geschehnissen in der Welt sowie der wichtigsten Ereignisse in den Leben bekannter Persönlichkeiten ermöglichen

    Binary RDF for Scalable Publishing, Exchanging and Consumption in the Web of Data

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    El actual diluvio de datos está inundando la web con grandes volúmenes de datos representados en RDF, dando lugar a la denominada 'Web de Datos'. En esta tesis proponemos, en primer lugar, un estudio profundo de aquellos textos que nos permitan abordar un conocimiento global de la estructura real de los conjuntos de datos RDF, HDT, que afronta la representación eficiente de grandes volúmenes de datos RDF a través de estructuras optimizadas para su almacenamiento y transmisión en red. HDT representa efizcamente un conjunto de datos RDF a través de su división en tres componentes: la cabecera (Header), el diccionario (Dictionary) y la estructura de sentencias RDF (Triples). A continuación, nos centramos en proveer estructuras eficientes de dichos componentes, ocupando un espacio comprimido al tiempo que se permite el acceso directo a cualquier dat

    Federated knowledge base debugging in DL-Lite A

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    Due to the continuously growing amount of data the federation of different and distributed data sources gained increasing attention. In order to tackle the challenge of federating heterogeneous sources a variety of approaches has been proposed. Especially in the context of the Semantic Web the application of Description Logics is one of the preferred methods to model federated knowledge based on a well-defined syntax and semantics. However, the more data are available from heterogeneous sources, the higher the risk is of inconsistency – a serious obstacle for performing reasoning tasks and query answering over a federated knowledge base. Given a single knowledge base the process of knowledge base debugging comprising the identification and resolution of conflicting statements have been widely studied while the consideration of federated settings integrating a network of loosely coupled data sources (such as LOD sources) has mostly been neglected. In this thesis we tackle the challenging problem of debugging federated knowledge bases and focus on a lightweight Description Logic language, called DL-LiteA, that is aimed at applications requiring efficient and scalable reasoning. After introducing formal foundations such as Description Logics and Semantic Web technologies we clarify the motivating context of this work and discuss the general problem of information integration based on Description Logics. The main part of this thesis is subdivided into three subjects. First, we discuss the specific characteristics of federated knowledge bases and provide an appropriate approach for detecting and explaining contradictive statements in a federated DL-LiteA knowledge base. Second, we study the representation of the identified conflicts and their relationships as a conflict graph and propose an approach for repair generation based on majority voting and statistical evidences. Third, in order to provide an alternative way for handling inconsistency in federated DL-LiteA knowledge bases we propose an automated approach for assessing adequate trust values (i.e., probabilities) at different levels of granularity by leveraging probabilistic inference over a graphical model. In the last part of this thesis, we evaluate the previously developed algorithms against a set of large distributed LOD sources. In the course of discussing the experimental results, it turns out that the proposed approaches are sufficient, efficient and scalable with respect to real-world scenarios. Moreover, due to the exploitation of the federated structure in our algorithms it further becomes apparent that the number of identified wrong statements, the quality of the generated repair as well as the fineness of the assessed trust values profit from an increasing number of integrated sources

    Locating and Accessing Data Repositories with WebSemantics

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    Many collections of scientific data in particular disciplines are available today on the World Wide Web. Most of these data sources are compliant with some standard for interoperable access. In addition, sources may support a common semantics, i.e., a shared meaning for the data types and their domains. However, sharing data among a global community of users is still difficult because of the following reasons: (i) data provides need a mechanism for describing and publishing available sources of data; (ii) data administrators need a mechanism for discovering the location of published sources and obtaining metadata from these sources; and (iii) users need a mechanism for browsing and selecting sources. This paper describes a system, WebSemantics, that accomplishes the above tasks. We describe an architecture for the publication and discovery of scientific data sources that is an extension of the World Wide Web architecture and protocols. We support catalogs containing metadata about data sources for some application domain. We define a language for discovering sources and querying their metadata. We then describe the WebSemantics prototype.</p
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